In his research on social mobility using surnames, Gregory Clark has found lower levels of social mobility than many other studies. Clark has defended this finding on the basis that analysis of social mobility across a single generation or using a single variable will overestimate it. Clark writes:

Conventional estimates of social mobility, which look at just single aspects of social status such as income, are contaminated by noise. If we measure mobility on one aspect of status such as income, it will seem rapid.

But this is because income is a very noisy measure of the underlying status of families. The status of families is a combination of their education, occupation, income, wealth, health, and residence. They will often trade off income for some other aspect of status such as occupation. A child can be as socially successful as a low paid philosophy professor as a high paid car salesman. Thus if we measure just one aspect of status such as income we are going to confuse the random fluctuations of income across generations, influenced by such things as career choices between business and philosophy, with true generalised social mobility.

A new paper published in the American Sociological Review by Chan and Boliver provides some evidence in favour of Clark’s argument. Chan and Boliver examined three British birth cohort studies comprising three generations of family members. They found that even after controlling for parental characteristics, grandparents still have a significant effect on their grandchild’s social position. From the abstract:

Net of parents’ social class, the odds of grandchildren entering the professional-managerial class rather than the unskilled manual class are at least two and a half times better if the grandparents were themselves in professional-managerial rather than unskilled manual-class positions. This grandparents effect in social mobility persists even when parents’ education, income, and wealth are taken into account.

Although I am referring to this work in support of Clark’s argument, which Clark suggests likely has a genetic (or something very like genetic) transmission component, Chan and Boliver do not reference inherited characteristics. They point to social factors such as grandparental resources and involvement in childrearing, and the possibility that those who have experienced a single generation of downward mobility may be better positioned or more motivated to bounce back.

We could take “better positioned” to implicitly refer to Clark’s argument, but we can state it more clearly. At least part of what Chan and Boliver are observing is social mobility minus the single-generation noise. Grandchildren of lower status parents but higher status grandparents are simply moving back toward the level of status that reflects their underlying characteristics. They are “better positioned” due to these characteristics and their higher motivation may well be one of those characteristics.

Chan and Boliver’s work also received some coverage from the BBC.

Links this week:

1. The Journal of Economic Behavior and Organization has a special issue out “Evolution as a General Theoretical Framework for Economics and Public Policy”. You can also access the papers through the Evolution Institute website and there is a series of summary articles in Evolution: This View of Life. Many look worth a read, and I’ll post about them over coming weeks/months.
2. David Sloan Wilson (one of the editors and authors in the JEBO special issue above) has an article in aeon magazine critiquing economics from an evolutionary angle.
3. Also in aeon magazine, an interesting take on obesity (HT: John Hawks). It’s fair to say that a simple “calorie in-calorie out” analysis doesn’t cut the mustard anymore. (But I don’t buy the bit about lab animal food staying the same).
4. Another article from Evolution: This View of Life that is worth a look – Daniel Hruschka on collectivism versus individualism.
5. Support for Gregory Clark’s argument that analysis of social mobility over a single generation overestimates its extent – a child’s socioeconomic position is determined by their grandparents, not just their parents (blog post on this article to come soon).
6. Britain is undergoing a baby boom. I expect this will be a common developed country observation over the next decade.

My first publication, Economic Growth and Evolution: Parental Preference for Quality and Quantity of Offspring, has just been released electronically in Macroeconomic Dynamics (ungated pdf here). With my co-authors Boris Baer and Juerg Weber, we simulate and extend Oded Galor and Omer Moav’s seminal paper Natural Selection and the Origin of Economic Growth (ungated working paper version here), the first paper that models an evolutionary trigger to the Industrial Revolution.

Galor and Moav’s model has two types of people in the population, each with a genetically inherited preference for quality or quantity of children. The quality-preferring genotype wants their children to have higher human capital, so they invest more in their education, while the quantity-preferring genotype is more interested in raw numbers.

During the long Malthusian era in which both genotypes struggle to earn enough to subsist (i.e. during the thousands of years leading up the Industrial Revolution), the quality-preferring genotype has a fitness advantage. As a quality-preferring genotype is of higher quality, they earn more income. This higher income is more than enough to cover education expenses, so they are also able to have more children than the quantity-preferring genotypes.

This fitness advantage leads the quality-preferring genotype to increase in prevalence. As this occurs, the increasing average level of education in the population drives technological progress. This in turn increases the incentive to invest in education, creating a feedback loop between technology and education.

Eventually, the rate of technological progress gets high enough to induce the quantity-preferring genotypes to invest in education also. When this happens, the average level of education jumps, boosting technological progress and causing the Industrial Revolution.

During this process, the population growth rate changes. Up to the time of the Industrial Revolution, population growth increases with technological progress, meaning that per capita income remains at the Malthusian level. However, when the level of technology leaps with the Industrial Revolution, the level of education becomes so high that population growth drops dramatically. A demographic transition occurs.

At the time of this transition, the relative fitness of the different types changes. After the Industrial Revolution, the quality-preferring genotypes invest so much into education that they have lower fertility than the quantity-preferring genotypes. The quality-preferring genotypes reduce in prevalence, their fitness advantage erased.

Galor and Moav worked through the dynamics of the model using phase diagrams. It is not particularly easy or intuitive to see the processes working together in their paper, so the first step in our paper is to simulate the model. This demonstrates the model’s feasibility, as well as showing the dynamics in a form that is easier to comprehend visually. In the chart below, you can see the dramatic jump in technological progress around generation 45 of the simulation, with per capita income growth also jumping at that time. Meanwhile, population growth drops to zero.

This second chart shows the change population composition. The quality-preferring genotype (genotype a) steadily increases in prevalence through to the Industrial Revolution, peaking at just under five per cent of the population. After the transition, it declines due to its lower fitness.

This change in selection pressure has an interesting implication. While natural selection is the trigger of the Industrial Revolution, the population composition before and after the transition is the same. There is no difference in population composition between developed and undeveloped countries. The only time there is a difference in population composition is during the transition, when the quality-preferring genotypes peak in prevalence. In some ways, the natural selection occurring in Galor and Moav’s model is a sideshow to the main event, the quality-quantity trade-off. In a similar model by Galor and Weil, a scale effect triggers the Industrial Revolution – that is, the concept that more people leads to more ideas, so technological progress increases with population growth.

That highlights the point where I am not convinced that the model describes what actually occurred. As far as human evolution relates to economic growth, I expect that inherent quality is at least (if not more) important than the quality-quantity trade-off. The Industrial Revolution was possible because higher quality (in an economic sense) people were selected for in the lead-up (with that lead up encompassing thousands of years). Further, for a man of low resources, his larger problem is convincing a woman to mate with him, not deciding on the right quantity-quantity mix.

The other thing that I should note is that, like most economic models, Galor and Moav’s model includes consumption with no clear evolutionary rationale (an issue I have discussed in an earlier post). Why do people in the model consume more than subsistence? If some people chose to focus all excess consumption into raising children they would come to dominate the population. This might be justified as being something to which the population has not yet adapted, but that explanation does not satisfy me.

Having made these quibbles, the model is still an impressive feat. It would not have been an easy task to create a model with technological progress, population and per capita income all following a path that resembles the last few thousand years of economic growth.

In our paper, we extend Galor and Moav’s model by considering the entry of people into the population that have a low preference for child quality – i.e. they weight child quantity more highly. Entry could be through migration or mutation. We show that if people with a low enough preference for quality enter the population, their higher fitness in the modern growth state can drive the economy back into Malthusian conditions.

We simulated a version of the model which had present in the initial population a genotype with a very low preference for educating their children. This strongly quantity-preferring genotype has a similar fitness to other genotypes that do not educate in the Malthusian state, and declines in prevalence while the quality-preferring genotype increases.

Once the economy takes off into the modern growth state, the strongly quantity-preferring genotype has the highest fitness as it dedicates the lowest proportion of its resources to educating its children. The strongly quantity-preferring genotype increases in prevalence until, eventually, the average level of education in the population plummets, undermining technological progress. The world returns to a Malthusian state, with high population growth eroding the income benefits of all earlier technological progress.

The following chart shows the rate of growth of population, technological progress and income per person. The first 70 generations look like the base model simulation shown above. However, after that point, technological progress plummets to zero. For the next 150 or so generations, population growth is positive, which can occur as per person income is above subsistence. Eventually, population growth drives income down to subsistence levels and population growth ceases.

In the next figure, you can see that the strongly quantity-preferring genotype, genotype c, grows from being a negligible part of the population to over 90 per cent prevalence. It is this change in population composition that drives the return to Malthusian conditions (you can also see the small peak in quality-preferring types around generation 45 that kicks off the Industrial Revolution). The strongly quantity-preferring genotypes educate their children far less than the other genotypes, depressing technological progress.

There is no escape from the returned Malthusian conditions. The quality-preferring genotype will have a fitness advantage in this new Malthusian state and will increase in prevalence. But although that increase in prevalence caused a take-off in economic growth the first time, this time there is no take-off. The strongly quantity-preferring types, which now dominate the population, cannot be induced to educate their children. They simply breed faster to take advantage of any technological progress spurred by the small part of the population that is educating their children.

This regression to Malthusian conditions could also be achieved by introducing the strongly quantity-preferring genotype into the simulation at other points in time, which might be representative of global migration. If it occurs after the Industrial Revolution, the timing of the return to Malthusian conditions will occur later. Short of restricting the range of potential quality-quantity preferences, there is no way to avoid the return to Malthusian conditions in this version of model. The strongly quantity-preferring genotypes will always have a fitness advantage when income is above subsistence and their population growth will drive income back down to subsistence levels.

One possible way to prevent a return to the Malthusian state is if there is also a scale effect in the population, whereby more people results in more ideas. This would give a basis for continuing growth after the take-off in population in the lead up to the transition. However, this scale effect must not be dependent on the level of education in the population, as that will still decline to zero. Further, if there is a scale effect, it raises the question of why the evolutionary trigger is required at all.

There are, of course, a few possible interpretations of the result that the economy returns to Malthusian conditions. The model or assumptions may be wrong. Humans may only have quality-quantity preferences in the growth promoting range. Or if we take Galor and Moav’s model seriously, modern levels of economic growth may be transient.

*I constructed this post out of two old posts I wrote when this work was first released as a working paper. The original posts with associated comments are here and here. The R code for simulating the model can be downloaded from this page.

In his seminal paper Population Growth and Technological Change: One Million B.C. to 1990, Michael Kremer combined two basic concepts to explain the greater than exponential population growth in human populations over the last million years.

The first concept is that more people means more ideas. A larger population will generate more ideas to feed technological progress.

The second concept is that, in a Malthusian world, population is constrained by income, with income a function of technology. Population can only increase if there is technological progress, with any increase in income generated by technological progress rapidly consumed by population growth.

When you combine these two concepts, a larger population generates more ideas, which in turn eases the constraint on additional population growth, which further accelerates the production of ideas. The result is population growth being in proportion to the population size. The following diagram illustrates the feedback loop.

When I first read Kremer’s paper, the title caught my attention, particularly the reference to One Million B.C. Humans have evolved markedly in the last one million years. One million years ago, Homo sapiens did not exist as a distinct species, with Homo erectus found in Africa, Europe and Asia. Since then, cranial capacity (a proxy for brain size) has increased from around 900 cubic centimeters to 1,350 cubic centimeters. And not only have humans evolved, but adaptive human evolution appears to be accelerating. As more people means more mutations, natural selection has greater material on which it can act.

It was this consideration that forms the basis of my latest working paper, Population, Technological Progress and the Evolution of Innovative Potential, co-authored with my supervisors Juerg Weber and Boris Baer.

In the spirit of Kremer’s original paper, we develop a model of population growth and technological progress, but add an extra element, which we call “innovative potential”. Innovative potential is any trait that results in the production of ideas that advance the technological frontier. Innovative potential might incorporate IQ, willingness to invest in innovation, participation in productive activities in which innovation may occur, risk preference, time preference and so on. At this stage, we do not specify the precise trait, but it is not hard to see what the likely traits are.

As more people means more mutations, mutations that increase the innovative potential of the population will occur with greater frequency in a larger population. As the population grows, so too does the rate of evolution of innovative potential.

Incorporating the evolution of innovative potential into the model creates a second element to the feedback loop, as is shown below. Population growth is now proportional to both the size and innovative potential of the population.

One of the more interesting results of the model can be seen when we partition the drivers of the acceleration of population growth between increasing population size and the increasing innovative potential of the population. As the population evolves, the relative contribution of continuing growth in innovative potential to the acceleration of population growth declines. Continuing population growth becomes the main driver of technological progress and further population growth. However, this does not mean that innovative potential is not important, as the level of innovative potential continues to have a material effect. Populations with higher innovative potential will have much faster population growth.

The reason this change occurs is that population growth is driven by both increasing population size and the increasing innovative potential of the population, whereas innovative potential only increases with population size. As the innovative potential reaches a higher level, each new person is more innovative and generates more ideas, but they will only generate the same number of mutations as they always have.

One issue with introducing innovative potential into a model of this kind is that ideas are non-excludable. Suppose I invent some new technology that increases my ability to procure resources. If someone else sees and copies this idea, I wont have an evolutionary edge. In the first version of the model presented in the paper, we handwave around this issue, and suggest that innovative people may have higher fitness due to prestige, the ability to keep secrets or some other avenue of reaping the benefits of the innovation. Although this handwaving likely has an element of truth, we introduced a version of the model in which those who are more innovative are also more productive in using those ideas. The results are robust to inclusion of this element.

One other observation from the model is the robustness of the population to technological shocks. Through human history, population did not undergo a simple increase, but underwent shocks and bottlenecks. For example, a change in climate could reduce the carrying capacity of the land (through reducing the effective level of technology), reducing population size.

In Kremer’s model, shocks of this nature are a strong setback to population growth and technological progress. As the population is smaller, idea production will be slower. In fact, population growth and technological progress will resemble the levels of growth when the population was last of that size. A population experiencing consistent technological shocks may never grow to a substantial size.

Where there is evolution of innovative potential, a technological shock is a setback to population growth, but the clock is not fully wound back to the time when the population was last of that size. The population now has higher innovative potential and the population recovers faster from each successive technological shock. This effect is particularly strong where higher innovative potential also increases the productivity of the population in using the new technologies.

Finally, two assumptions that we include in the model are that population instantaneously adjusts to the carrying capacity of the land, and that the spread of mutations is instantaneous. The first is a weak assumption given the time spans over which the model operates. The second is much stronger. As a result, we also consider the time it takes for a mutation to spread through the population in a dynamic model and an agent-based simulation. Delaying the spread of a mutation does not substantively change the model results, although it prevents an explosion in the innovative potential of the population at the time that the population explodes. But as noted above, even where mutations spread instantaneously, the contribution of continuing evolution of innovative potential to the acceleration of population growth drops to near zero when the population explodes. The delay in the spread of mutations simply strengthens that result.

If you would like to play with the agent-based model, code for the model is contained at the end of the working paper, or you can download the model here. I developed the model in NetLogo, an open source agent-based programming environment, which you can download from here.

Links this week:

1. If you read one thing this week, read this article by Enrico Spolaore and Romain Wacziarg: How Deep Are the Roots of Economic Development? (ungated version here). I’ll post in more detail on the article in a couple of weeks.
2. Henry Harpending on health after the Neolithic revolution.
3. Noah Smith provides a list of essential papers in behavioural finance.
4. Cannabis and IQ.
5. My newest working paper has just gone online: Population, Technological Progress and the Evolution of Innovative Potential. I’ll post about it sometime next week. [Post is now up here]

In my last post, I noted R.A. Fisher’s argument that a larger population leads to more mutations and greater potential for adaptive evolution. As human populations have undergone massive growth over recent tens of thousands of years, we would expect the evidence of this population growth to show in our genomes. In this post, I point to a couple of papers that look at this evidence.

In the first, Recent acceleration of human adaptive evolution, John Hawks and colleagues examined the age distribution of positively selected gene variants in a 3.9 million SNP dataset. To determine the variant (allele) ages, they examined “linkage blocks”. When an SNP is selected, it tends to carry with it other polymorphisms on the chromosome around it. If a variant tends to have a similar set of polymorphisms around it, while another variant at that same point does not, this suggests that the first is under positive selection, dragging along its neighbouring polymorphisms with it (the linkage block). The older original variant will have a bigger mix of polymorphisms around it through reshuffling over time.

This paper has two great charts that illustrate the findings. The first compares the age distribution of the variants in African and European populations. In both populations, the peak in variants is observed in recent history, which matches a theory of mutations increasing with population. Interestingly, we see an earlier peak for the African population. Africa had a larger late Pleistocene population, while population in Europe and West Asia took off after the Neolithic revolution.

The second chart plots the observed variant ages against two models: one with constant mutation rates, and a second where mutations rates increase with population size. The second model provides a far better (but not perfect) fit to the observed data.

You can also read more on this paper (written around the time it came out) on John Hawks’s blog and at Gene Expression.

The second paper by Fu and colleagues, published in Nature Genetics, contained analysis of the age of over one million single nucleotide variants across 6,500 people. Fu and colleagues found that most of the variants that they analysed were relatively young, with the majority arising in the last 5,000 to 10,000 years (as would be expected if mutations increase with population). The age of the variants is best shown in the following chart which is a heat map of the variants in African and European populations before and after recent accelerated population growth (i.e. before or within the last 5,000 years).

While Fu and colleagues don’t transfer this story of the growth in rare variants to one of adaptive evolution, you can see how it fits with the first paper by Hawks and colleagues. These variants, even though most may be deleterious, provides the store of variation on which evolution can act.

I posted a round-up of some other blog posts on this second article at the end of last year when the article underwent initial electronic release. The post by John Hawks is very good.

*The reason I’m covering this territory is to lay the groundwork for a post on my latest working paper: Population, Technological Progress and the Evolution of Innovative Potential. You can now find that post here.

A core ideas in economics is that more people means more ideas. To take an extreme case, you would expect a population of one person to generate fewer ideas that a population of one million people. The precise relationship between population and ideas depends on factors such as the fishing-out of ideas, network effects, the composition of the population and the like, but it would seem to be strongly positive.

When you combine this assumption with the Malthusian concept that the level of technology constrains population, a larger population grows faster than a smaller population as a larger population generates more ideas to ease this Malthusian constraint. Michael Kremer used this argument to explain the greater than exponential population growth of the last million or so years (although that pattern has broken down since 1950).

This argument has a counterpart in evolutionary biology. More people means more mutations. From R.A. Fisher (1930):

The great contrast between abundant and rare species lies in the number of individuals available in each generation as possible mutants. The actual number of mutations in each generation must therefore be proportional to the population of the species. With mutations having appreciable mutation rates, this makes no difference, for these will reach an equilibrium with counterselection at the same proportional incidence. The importance of the contrast lies with the extremely rare mutations, in which the number of new mutations occurring must increase proportionately to the number of individuals available. It is to this class, as has been shown, that the beneficial mutations must be confined, and the advantage of the more abundant species in this respect is especially conspicuous.

The greater number of mutations then provides more variation on which natural selection can act. Larger groups will, other things being equal, experience faster evolutionary change. Fisher again:

The theoretical deduction that the actual number of a species is an important factor in determining the amount of variance which it displays, thus seems to be justified by such observations as are at present available. Its principal consequence for evolutionary theory seems to be that already inferred by Darwin, that abundant species will, ceteris paribus, make the most rapid evolutionary progress, and will tend to supplant less abundant groups with which they come into competition. We may infer that in the ordinary condition of the earth’s inhabitants a large number of less abundant species will be decreasing in numbers, while a smaller number of more abundant species will be increasing …

Combining these two concepts – more people means more ideas and more mutations – gives larger human populations a double advantage over a long-term horizon. The higher level of production of ideas and beneficial mutations provides two avenues from which large populations can continue to grow.

Links this week:

1. Malcolm Gladwell on Albert O. Hirschman. One good paragraph:

People don’t seek out challenges, he went on. They are “apt to take on and plunge into new tasks because of the erroneously presumed absence of a challenge—because the task looks easier and more manageable than it will turn out to be.” This was the Hiding Hand principle—a play on Adam Smith’s Invisible Hand. The entrepreneur takes risks but does not see himself as a risk-taker, because he operates under the useful delusion that what he’s attempting is not risky.

2. This list of book recommendations from Nassim Taleb is from early last year, but is definitely worth a read.
3. Does The Hunter-Gatherer Style Of Education Work?
4. I came across the idea of pathological altruism when Barbara Oakley presented at last year’s Consilience Conference. I don’t like the idea, and neither does Robert Kurzban.
5. A potentially interesting new book: The Handbook of Rational Choice Social Research

I’m not sure if the format of the Dutch postcode lottery is common, but it certainly creates some interesting incentives. In this lottery, a random postcode is drawn from the 430,000 postcodes in the Netherlands, with each postcode having, on average, 19 households. Each person in that postcode who has purchased a ticket in the lottery receives €12,500 for each ticket that they hold (people can buy more than one ticket), and one ticket in the postcode is awarded a BMW.

If your postcode is drawn but you do not own a ticket, you know with certainty that you would have won if you had purchased one. You will also know that there were other winners in your postcode, and given the typically small size of a postcode, it is likely that you know some of those winners. With 30% of the Dutch participating in the lottery, there is a good chance that one of those winners is your neighbour.

It was with information from this lottery that Peter Kuhn and colleagues decided (ungated working paper here) to look at two of the more interesting questions in economics. First, how do people react to a wealth shock? Do they smooth consumption over their lifespan, or do they blow it all at once? Second, how do other people react to this change in relative wealth? If my neighbour wins the lottery, does it change my behaviour even though it does not affect my absolute well-being?

Kuhn and colleagues obtained their data by surveying people covering four groups – winning lottery participants, people who lived in a postcode that won but who had not purchased a lottery ticket, lottery participants in postcodes who had not won, and those who do not play the lottery from those unsuccessful postcodes. This combination allowed the researchers to compare behaviour of those with and without lottery tickets in winning postcodes, while controlling for differences in characteristics between those who play the lottery or not by examining differences in unsuccessful postcodes. The researchers asked questions about a range of factors, including household composition, demographic variables, labor supply, happiness, car ownership, income and lottery participation. Questions were asked about both current behaviour and behaviour a year earlier, which was intended to capture behaviour six months before and after the lottery result.

On the question of how the income shock affects the behaviour of the lottery winner, the results supported the idea that people smooth consumption over their lifecycle as winning the lottery had no effect on most household expenditure. What they did find, however, was an increase in car and durable purchasers by the winners, which supports the idea that shifting the timing of purchases of durables is one way in which people smooth consumption. It is also consistent with the idea that people have self-imposed borrowing constraints.

The result for which this study is more famous is the effect on the neighbours. Having a neighbour win the lottery increases the unsuccessful person’s probability of purchasing a car in the next six months by around 7 per cent, which the authors consider large relative to the size of the effects on the lottery winners themselves. This also reduces the average age of their car (no surprise given they are more likely to have purchased a new one). Interestingly, there was no effect on happiness for either the lottery winners or their neighbours, which is problematic for a relative income based model of happiness.

It’s not easy to interpret this result. That the increase in consumption by neighbours occurred only in relation to a visible item of consumption – a car that the neighbour will surely see – is suggestive of what the authors call “keeping up with the van den Bergs”, but the transmission path is unclear. Are they copying the purchase of a car, or responding to the known change in relative income? I sense that the dynamic between a neighbour who knows they missed out on a certain lottery win if they had bought a ticket and their triumphant neighbour would be much different to the case where that income shock was from another source.

I’d like to see two separate experiments, one involving knowledge that a neighbour won the lottery (but without the car or durable expenditure by the winner) and a second with only the signs of expenditure. My (speculative) suspicion is that the lottery knowledge would dominate. An experiment tracking neighbour responses to new car purchases might also be interesting, as if visible consumables are what the neighbour responds to, the windfall may not be strictly required.

As an end note, I’d heard about this paper before I got around to reading it, and I was surprised at the gap between what I had heard and the strength of the author’s claims. I’d seen this result waved around a lot as a sign that people respond to relative income changes, but there are some complicating factors that make it difficult to interpret the causative pathway. The happiness result also does not help the “relative income matters” case (nor possibly my speculative suspicion).

For the last year or so, I have had sitting in my “to blog” pile a 2004 New Yorker article about the increasing height of Europeans relative to Americans. It has a lot of interesting content. It talks about how height peaked in Europe around 800 AD, before declining through to 1700 (largely associated with the rise of cities), and then commencing an upward climb. It notes how Mexican-American teenagers have now equalled the United States norm, while American Mayan teenagers have gained four inches on Guatemalan Mayan teenagers in around two decades. The overarching point of the article is also interesting, that being the failure of heights to increase in the United States (after screening for issues around immigration, race etc.) since the 1950s while European heights continue to rise.

I’ve delayed putting a post up as I’m still getting my head around a lot of the research in the area, and I’m not sure if the main concern was still current following more recent studies. But some recent events have triggered me to put together this post despite still not being fully across the area. The triggers include some comments following my recent post on obesity (to which those observations on Latin American height directly relate), the death of Robert Fogel, a passage in Marlene Zuk’s Paleofantasy and some comments by James Flynn in Are We Getting Smarter?.  So, here are a few interesting snippets.

Robert Fogel is interviewed in the New Yorker article, including about his work Time on the Cross: The Economics of American Slavery:

Historians had long insisted that slavery was not only inhuman; it was bad business—hungry, brutalized workers made the poorest of farmers. Fogel and Engerman found nearly the opposite to be true: Southern plantations were almost thirty-five per cent more efficient than Northern farms, their analysis showed. Slavery was a cruel and inhuman system, but more so psychologically than physically: to get the most work from their slaves, planters fed and housed them nearly as well as free Northern farmers could feed and house themselves. …

Steckel decided to verify his mentor’s claims by looking at the slaves’ body measurements. He went through more than ten thousand slave manifests—shipboard records kept by traders in the colonies—until he had the heights of some fifty thousand slaves; then he averaged them out by age and sex. The results were startling: adult slaves, Steckel found, were nearly as tall as free whites, and three to five inches taller than the average Africans of the time.

The height study both redeemed and rebuked “Time on the Cross.” Although the adult slaves were clearly well fed, the children were extremely small and malnourished. (To eat, apparently, they had to be old enough to work.) But Fogel was more than willing to stand corrected.

From Zuk’s Paleofantasy, a suggestion that the costs to stature of the shift to agriculture were only transitory while humans adapted to the new diet:

The skeletons of ancient farmers are filled with evidence of tooth decay, iron deficiency anemia, and other disorders. Diamond notes that the Greek and Turkish skeletons from preagricultural sites averaged 5 feet 9 inches in height for men and 5 feet 5 inches for women, but after farming became established, people were much shorter—just 5 feet 3 inches and 5 feet, respectively, by about 5,000 years ago, probably because they were suffering from malnutrition. The teeth from skeletons of Egyptians who died 12,000 years ago, about 1,000 years after their people had shifted from foraging to farming, were rife with signs of malnutrition in the enamel: a whopping 70 percent of them, up from 40 percent before agriculture became widespread.

Then a funny thing happened on the way from the preagricultural Mediterranean to the giant farms of today: people, at least some of them, got healthier, presumably as we adapted to the new way of life and food became more evenly distributed. The collection of skeletons from Egypt also shows that by 4,000 years ago, height had returned to its preagricultural levels, and only 20 percent of the population had telltale signs of poor nutrition in their teeth. Those trying to make the point that agriculture is bad for our bodies generally use skeletal material from immediately after the shift to farming as evidence, but a more long-term view is starting to tell a different story. For example, Timothy Gage of the State University of New York at Albany examined long-term mortality records from around the world, along with the likeliest causes of death, and concluded that life span did not decrease, nor did many diseases increase, after agriculture. Some illnesses doubtless grew worse after humans settled down, but life has had its “nasty, brutish, and short” phases at many points throughout history.

In Are We Getting Smarter? Flynn offers some thoughts on whether height and IQ gains have a common cause in improved nutrition:

The connection between height gains and IQ gains over time is significant only because it may signal nutrition as a common cause. Coupled with the assumption that nutritional gains have affected the lower classes disproportionately, this brings us back to the IQ curve. Wherever height gains persist, presumably nutritional gains persist, and where nutritional gains persist, IQ gains should show the predicted pattern, that is, gains mainly in the lower half of the curve.

This is not always the case. Martonell (1998) evidences that height gains persisted in the Netherlands until children born about 1965. Yet, cohorts born between 1934 and 1964 show massive Raven’s-type gains throughout the whole range of IQs. The French gained in height until at least those born in 1965. Yet, cohorts born between 1931 and 1956 show massive Raven’s gains that were uniform up through the 90th percentile. …

Norway … counts against the posited connection between height gains and IQ gains. The upper classes tend to be taller. Yet, height gains have been larger in the upper half of the height distribution than in the lower half (Sundet, Barlaug, & Torjussen, 2004). This combination, greater height gains in the upper half of the distribution, greater IQ gains in the lower, poses a serious problem. Are there two kinds of enhanced nutrition, one confined to the upper classes that raises height more than it does IQ, the other affecting the lower classes that raises IQ more than it does height?

If the above is of interest, also have a glance at an earlier post of mine on Fogel, which was triggered by a NYT profile in 2011.